Enhancing Cataract Detection Using Machine Learning Algorithms

Manuscript submitted December 22, 2025; accepted December 31, 2025; published January 27, 2026

Abstract—Cataracts are a common eye ailment that can cause visual impairment if not diagnosed and treated early. Cataract detection using machine learning, specifically decision tree classifiers, offers a promising approach for the early identification of cataracts in human eyes. By analyzing features extracted from eye images, the study achieved high accuracy in predicting cataract presence, providing a reliable method for timely intervention and treatment to preserve vision health. Conventional methods of diagnosing cataracts frequently depend on the subjective assessments of ophthalmologists and tests of visual acuity. These methods, however, can be inconsistent and might miss cataracts that are still in the early stages. By utilizing large databases of ocular images and computer vision techniques, machine learning provides a workable solution for cataract detection.

keywords—Computer vision, machine learning, decision tree, cataract detection, algorithms

Cite: Shankar M. Patil, Soheb Dalvi, Anish Rane, Avadhut Mulaye, Satyaprakah Tiwari,"Enhancing Cataract Detection Using Machine Learning Algorithms," Journal of Advances in Artificial Intelligence, vol. 4, no. 1, pp. 11-23, 2026. doi: 10.18178/JAAI.2026.4.1.11-23

Copyright © 2026 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).